• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.39-42
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• 2000

Both evolutionary programming (EP) and genetic algorithm (GA) are classified as evolutionary computation. They have been successful in finding a solution in a wide search space. In this paper, our objectives are to find the coefficients of characteristic equation, in terms of the stability indices using EP and GA, and to make a comparison of both methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.917-923
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• 1999

This paper proposes an efficient method of hydro-thermal scheduling in coordination with head variation and hydraulically coupled plants using Evolutionary Programing(EP). Based on the EP technique, the proposed algorithm is capable of determining the global optimal solutions. The constraints such a power balance condition, water available condition and transmission losses are embedded and satisfied throughout the proposed EP approach. The effectiveness of the proposed approach is demonstrated on the test systems and compared to those of other method. The results show that the new approach obtains a more highly optimal solutions than the conventional other methods such as newton-raphson method, Dynamic Programming(DP), LU factorization.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.61-66
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• 2005

This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.542-547
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• 2000

진화프로그래밍(Evolutionary Programming : EP)은 최적화 문제에 있어서 매우 유용한 기법으로 자연선택의 원리를 모방한 탐색알고리즘이다. EP는 기존의 최적화 알고리즘에 비하여 여러해를 동시에 탐색하는 전역탐색(global search)방법이므로 국부수렴(local convergence)의 가능성이 줄어들고, 최적화 파라메터 영역의 연속성과 미분치의 존재성과 같은 조건이 필요 없는 장점을 갖는다. 이러한 장점에도 불구하고, EP의 탐색영역이 초기조건 및 최적화 파라메터들의 랜덤 생성 그리고 최적화에 필요한 전략적 파라메터들에 의하여 탐색 영역이 결정되고, 수렴성이 느린 단점을 갖는다. 이러한 문제를 해결하기 위하여, 본 연구에서는 빠른 수렴성과 다양성을 갖는 개선된 EP을 제안하고, 제안된 방향성 벡터를 갖는 개선된 EP를 함수 최적화 문제에 적용하여 그 성능의 유용성을 보이고자 한다.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.714-716
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• 1996

This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.673-680
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• 2002

This paper proposes a method for detecting edge using an evolutionary programming and a momentum back-propagation algorithm. The evolutionary programming does not perform crossover operation as to consider reduction of capability of algorithm and calculation cost, but uses selection operator and mutation operator. The momentum back-propagation algorithm uses assistant to weight of learning step when weight is changed at learning step. Because learning rate o is settled as less in last back-propagation algorithm the momentum back-propagation algorithm discard the problem that learning is slow as relative reduction because change rate of weight at each learning step. The method using EP-MBP is batter than GA-BP method in both learning time and detection rate and showed the decreasing learning time and effective edge detection, in consequence.